latent variable modeling of neuropathology data: implications for collaborative science
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Latent Variable Modeling of Neuropathology Data: Implications for Collaborative Science. Dan Mungas University of California, Davis. Acknowledgements. Funded in part by Grant R13 AG030995 from the National Institute on Aging - PowerPoint PPT PresentationTRANSCRIPT
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Latent Variable Modeling of Neuropathology Data:
Implications for Collaborative Science
Dan MungasUniversity of California, Davis
Friday Harbor Psychometrics, 2013
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Acknowledgements
• Funded in part by Grant R13 AG030995 from the National Institute on Aging
• The views expressed in written conference materials or publications and by speakers and moderators do not necessarily reflect the official policies of the Department of Health and Human Services; nor does mention by trade names, commercial practices, or organizations imply endorsement by the U.S. Government.
Friday Harbor Psychometrics, 2013
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Collaborative Science
Friday Harbor Psychometrics, 2013
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Friday Harbor Psychometrics, 2013
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Latent Variable Modeling
Friday Harbor Psychometrics, 2013
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• Now what is the message there? The message is that there are no "knowns." There are things we know that we know. There are known unknowns. That is to say there are things that we now know we don't know. But there are also unknown unknowns. There are things we do not know we don't know. So when we do the best we can and we pull all this information together, and we then say well that's basically what we see as the situation, that is really only the known knowns and the known unknowns. And each year, we discover a few more of those unknown unknowns. ~ D. Rumsfeld, June 6, 2002
Friday Harbor Psychometrics, 2013
The Essence of Latent Variable Modeling
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Neuropathology
Friday Harbor Psychometrics, 2013
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Friday Harbor Psychometrics, 2013
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Friday Harbor Psychometrics, 2013
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Friday Harbor Psychometrics, 2013
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Neurofibrillary tangles and neuritic plaques
Neuritic Plaques
Neurofibrillarytangles
Friday Harbor Psychometrics, 2013
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Friday Harbor Psychometrics, 2013
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Measurement Challenges in Neuropathology
• Sampling of brain regions• Reliability and standardization of methods for
quantitation• Distribution of variables• Relation to clinical and cognitive outcomes
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Distribution Issues
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Friday Harbor Psychometrics, 2013
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Friday Harbor Psychometrics, 2013
Sophisticated Tools for Item Scaling
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Neurofibrillary tangles and neuritic plaques
Neuritic Plaques
Neurofibrillarytangles
Friday Harbor Psychometrics, 2013
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Friday Harbor Psychometrics, 2013
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Practical Approaches to Modeling Neuropathology
• Many modeling approaches are based on assumption of multivariate normality
• Modeling neuropathology counts as continuous variables can be problematic Use of robust distribution free estimators does
not solve problem• Latent variable modeling approaches for
categorical/ordinal data can be helpful
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Friday Harbor Psychometrics, 2013
Categorical Variable Modeling Example
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Categorical Data Issues
• Recoding of data required to create “manageable” number of categories Does this result in loss of information? Are there other/better approaches?
• Count variables modeled using different distributional assumptions
• Bayesian estimation
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Applications of Latent Variable Modeling to Neuropathology Studies
Friday Harbor Psychometrics, 2013
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CFI = .988TLI = .994RMSEA = .076WRMR =.738
mfrnp
mtmpnp
inparnp
hipponp
entonp
mfrdp
mtmpdp
inpardp
hippodp
entodp
mfrnft
mtmpnft
inparnft
hipponft
entonft
Neur-Plq
Diff-Plq
Cort-NFT
MT-NFT
.89
.87
.92
.75
.87
.83
.83
.91
.94
.91
.89
.93
.85
.89
.73
.80
.58
.71
.68
.48
.77
2 = 124.9, df = 39
4 Dimension Measurement Model – AD NeuropathologyReligious Order Study
Friday Harbor Psychometrics, 2013
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MedialTemporalTangles
NeoCorticalTangles
NeuriticPlaques
ENT
HC MF
IP
MT
ENT
HC
MF
IP
MT
DiffusePlaques
ENT
HC MF
IP
MTFriday Harbor Psychometrics, 2013
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MedialTemporalTangles
NeoCorticalTangles
NeuriticPlaques
ENT
HC MF
IP
MT
ENT
HC
MF
IP
MT
DiffusePlaques
ENT
HC MF
IP
MT
Age
APOE
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HC
MF
MedialTemporalTangles
NeoCorticalTangles
NeuriticPlaques
Age
APOE
ENT
HC MF
IP
MT
ENT
IP
MT
DiffusePlaques
ENT
HC MF
IP
MT
0.84
0.58
0.32
0.26
0.40
0.18
0.36
0.77
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Study 2 - MAS
Study 1 - ROS
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Neuropathology and Cognition – Religious Order Study & Memory and Aging Project
N = 652, Dowling et al., 2011Friday Harbor Psychometrics, 2013
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GWMSUBCMIC
KDPCBRALWM
GWMCMIC
KWMCIMIC
KGMCPAFMI
KGMCUNFMI
KGMCMUFMI
KGMSUBCM
WM_ISCH
KWMPERIV
KGMSUBCL
KWMCICYS
KWMCILAC
GWMSUBCLAC
KGMCMUCIV
KGMCUNCIV
KGMCPACIV
GWMCCYS
White MatterIncomplete Infarction
CorticalInfarcts
MicroInfarcts
Sub-CorticalInfarcts
0.80
0.77
0.84
0.78
0.91
0.64
Model Fit: CFI: 0.994 RMSEA: .022
Friday Harbor Psychometrics, 2013
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Mixed Effects Modeling of Neuropathology Effects on Longitudinal Trajectories
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CASI and NeuropathologyHonolulu Asian Aging Study
• Random Effects Model• Dependent Variable
CASI• Estimated score at death• Rate of change preceding death
• Independent Variables Neuritic Plaque Factor Score Neurofibrillary Tangle - Neocortical Factor Score Neurofibrillary Tangle - Medial Temporal Factor Score Estimated Brain Atrophy
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Estimated CASI at Death
Effect Coef. S.E. p
Intercept 75.92 0.84 .001
NPL -2.01 1.26 .11
NFT-NC -3.10 1.25 .01
NFT-MT -.44 1.07 .68
Brain Atrophy -6.43 .83 .001
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Estimated CASI Change
Effect Coef. S.E. p
Intercept 75.92 0.84 .001
NPL .44 .18 .01
NFT-NC .19 .19 .32
NFT-MT -.35 .15 .02
Brain Atrophy .31 .12 .009
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Braak and Vascular Risk TrajectoriesEpisodic Memory
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Braak and Vascular Risk TrajectoriesExecutive Function
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The Internet
• A global to-do list that anyone in the world can add to, especially Rich Jones.
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